A market for social-media data Sippingfrom the fire hose

　　社交媒體數(shù)據(jù)的市場 細(xì)品弱水三千

　　Making sense of a torrent of tweets

　　從推文的洪流中找尋意義

　　MOST tweets are inane, but a million may contain valuable information.

　　多數(shù)推文單看起來沒有什么重要意義，但是一百萬條一起就可能含有價值不菲的信息。

　　Fed through clever algorithms, a torrent of microblogs can reveal changes in a nation smood.

　　通過聰明的算法，微博的洪流可以揭露一個國家的情緒變化。

　　Hence the excitement about a new market: the sale and analysis of real-time social-mediadata.

　　于是，對于一個新興市場社交媒體數(shù)據(jù)實時銷售和分析的興奮就產(chǎn)生了。

　　DataSift, a start-up, will soon launch a marketplace for such information.

　　DataSift是一家創(chuàng)業(yè)型企業(yè)，它不久將推出一個針對這樣的信息的市場。

　　Analysing social media used to be a cottage industry.

　　社交媒體的數(shù)據(jù)分析曾經(jīng)是家庭作坊式的小本生意。

　　Firms gathered data slowly and patchily, through mechanisms not built for the purpose.

　　企業(yè)從非專屬渠道緩慢而零散的收集數(shù)據(jù)。

　　Many online services kept their data locked up, because there was no way to make moneyfrom them. All this is changing.

　　很多在線服務(wù)商將它們的數(shù)據(jù)束之高閣，因為以前沒有方法可以從中謀利。所有的這一切都在改變。

　　Twitter was the first to move because it generates ever more data: the number of tweets perday now exceeds 230m, up more than 100% from the beginning of the year.

　　推特是先行者，因為它產(chǎn)生了如此多的數(shù)據(jù)：每天有超過2.3億條推產(chǎn)生，比年初多一倍多。

　　Twitter would like to turn its popularity into money, but rather than beefing up its owninfrastructure, it plans to outsource the task of distributing and selling its data to DataSift andGnip, another start-up.

　　推特希望把它的人氣變成現(xiàn)金，但它并沒有增加自己的基礎(chǔ)設(shè)施建設(shè)，而是計劃把分配和出售數(shù)據(jù)的任務(wù)外包給DataSift和Gnip。

　　Both DataSift and Gnip are striving to be data platforms .

　　DataSift 和Gnip都在為成為 數(shù)據(jù)平臺 而努力。

　　They collect and standardise information from all kinds of social-media servicesnot onlyTwitter, but also Facebook, YouTube and others.

　　它們從各種社交媒體服務(wù)不僅是推特，也包括臉書，YouTube和其他收集、整理信息并使其標(biāo)準(zhǔn)化。

　　Both Gnip and DataSift have built robust networks which can cope with massive amounts ofdata in real time.

　　Gnip和DataSift都已經(jīng)建立可靠網(wǎng)絡(luò)用以對付實時的大規(guī)模數(shù)據(jù)。

　　And both are enforcing licensing rules: for instance, that a stream of tweets can be analysedbut not republished.

　　兩家也都在實施許可規(guī)則：比如，一系列的推文可以用來分析，但是不能再次將其發(fā)布。

　　Gnip, based in Boulder, Colorado, is more of a wholesale distributor. It charges $33,000 amonth for a feed of half of all tweets.

　　位于科羅拉多的博爾德的Gnip公司更大程度上是一個批發(fā)型分銷商。

　　Customers can also subscribe to feeds of tweets containing web links or certain keywords.

　　訂閱全部推文的一半的消息源，要收3.3萬美元每月。

　　Buyers are mostly social-media monitoring companies, which analyse the data for a fee.

　　客戶也可以訂閱包含網(wǎng)址或某些特定關(guān)鍵字的消息源。

　　Sysomos, a Canadian firm, for example, allows firms to track in real time what people thinkabout certain products.

　　買方通常是社交媒體監(jiān)控企業(yè)，它們靠分析數(shù)據(jù)收費賺錢。例如，Sysomos是一個加拿大公司，它可以讓企業(yè)實時追蹤人們對于特定產(chǎn)品的感受。

　　DataSift serves both big corporations and individuals.

　　DataSift的服務(wù)對象包括大企業(yè)和個人。

　　Customers can define sophisticated filters, for instance to find all tweets by men who areinterested in a new product and live in London.

　　客戶可以定義復(fù)雜的過濾條件，例如找到對一個新產(chǎn)品有興趣，且在倫敦生活的男性所發(fā)的所有推文。

　　Charges for DataSift depend on the filter s complexity and the amount of data delivered.

　　DataSift的費用取決于過濾條件的復(fù)雜程度和交付的數(shù)據(jù)流量。

　　The streams from Gnip and DataSift can be combined with data from more specialised firmsthat try to extract meaning from social-media data.

　　Gnip和DataSift的數(shù)據(jù)流可以與更專業(yè)的企業(yè)的數(shù)據(jù)結(jié)合，那些更專業(yè)的企業(yè)試圖從社交媒體數(shù)據(jù)中提取出意義。

　　Lexalytics, for instance, analyses the sentiment of messages and posts.

　　比如，Lexalytics分析消息和帖子的情緒。

　　Klout measures the influence of social-media users .

　　Klout則測量社交媒體用戶的影響力。

　　Having a marketplace such as DataSift has already encouraged other social-media servicesto open their data vaults, says Nick Halstead, the founder of DataSift.

　　DataSift的創(chuàng)始人尼克?霍爾斯特德說，有了像DataSift那樣的數(shù)據(jù)交易市場，使得更多的社交媒體服務(wù)商開啟其數(shù)據(jù)的倉庫。

　　Financial firms have become interested in feeding such data into the algorithms they use tomake investment decisions, says Chris Moody, Gnip s president.

　　Gnip的總裁克里斯?穆迪說，金融企業(yè)對此也有興趣：將這樣的數(shù)據(jù)放到他們的算法里，來做出投資決策。

　　And corporations are increasingly keen on combining social-media data with customerinformation.

　　企業(yè)越來越熱衷于將社交媒體數(shù)據(jù)和客戶信息相結(jié)合。

　　Yet growth in this market could be held backby privacy concerns.

　　然而，對于隱私的擔(dān)憂可能會阻礙這個市場的發(fā)展。

　　Most people think that tweets are only up to 140 characters long.

　　多數(shù)人認(rèn)為一條推文最多只有140個字符那么長。

　　But those who sip from Twitter s fire hose can get much more information, including asender s location, the biography on his profile page and how many people have subscribedto his messages .

　　但是那些從推特的數(shù)據(jù)洪流中 細(xì)細(xì)品味 的人，可以從中得到更多的信息，包括發(fā)送者的所在位置，從個人介紹頁面得到的檔案信息，以及有多少人訂閱了他的信息。

　　Most of this information is freely available on Twitter s website.

　　多數(shù)這樣的信息都是可以從推特的網(wǎng)站上免費獲取的。

　　But if users realise how their data are used, they may clam up.

　　但是，一旦用戶意識到他們的數(shù)據(jù)是怎樣被使用的，他們可能就會緘口不言了。

　　A market for social-media data Sippingfrom the fire hose

　　社交媒體數(shù)據(jù)的市場 細(xì)品弱水三千

　　Making sense of a torrent of tweets

　　從推文的洪流中找尋意義

　　MOST tweets are inane, but a million may contain valuable information.

　　多數(shù)推文單看起來沒有什么重要意義，但是一百萬條一起就可能含有價值不菲的信息。

　　Fed through clever algorithms, a torrent of microblogs can reveal changes in a nation smood.

　　通過聰明的算法，微博的洪流可以揭露一個國家的情緒變化。

　　Hence the excitement about a new market: the sale and analysis of real-time social-mediadata.

　　于是，對于一個新興市場社交媒體數(shù)據(jù)實時銷售和分析的興奮就產(chǎn)生了。

　　DataSift, a start-up, will soon launch a marketplace for such information.

　　DataSift是一家創(chuàng)業(yè)型企業(yè)，它不久將推出一個針對這樣的信息的市場。

　　Analysing social media used to be a cottage industry.

　　社交媒體的數(shù)據(jù)分析曾經(jīng)是家庭作坊式的小本生意。

　　Firms gathered data slowly and patchily, through mechanisms not built for the purpose.

　　企業(yè)從非專屬渠道緩慢而零散的收集數(shù)據(jù)。

　　Many online services kept their data locked up, because there was no way to make moneyfrom them. All this is changing.

　　很多在線服務(wù)商將它們的數(shù)據(jù)束之高閣，因為以前沒有方法可以從中謀利。所有的這一切都在改變。

　　Twitter was the first to move because it generates ever more data: the number of tweets perday now exceeds 230m, up more than 100% from the beginning of the year.

　　推特是先行者，因為它產(chǎn)生了如此多的數(shù)據(jù)：每天有超過2.3億條推產(chǎn)生，比年初多一倍多。

　　Twitter would like to turn its popularity into money, but rather than beefing up its owninfrastructure, it plans to outsource the task of distributing and selling its data to DataSift andGnip, another start-up.

　　推特希望把它的人氣變成現(xiàn)金，但它并沒有增加自己的基礎(chǔ)設(shè)施建設(shè)，而是計劃把分配和出售數(shù)據(jù)的任務(wù)外包給DataSift和Gnip。

　　Both DataSift and Gnip are striving to be data platforms .

　　DataSift 和Gnip都在為成為 數(shù)據(jù)平臺 而努力。

　　They collect and standardise information from all kinds of social-media servicesnot onlyTwitter, but also Facebook, YouTube and others.

　　它們從各種社交媒體服務(wù)不僅是推特，也包括臉書，YouTube和其他收集、整理信息并使其標(biāo)準(zhǔn)化。

　　Both Gnip and DataSift have built robust networks which can cope with massive amounts ofdata in real time.

　　Gnip和DataSift都已經(jīng)建立可靠網(wǎng)絡(luò)用以對付實時的大規(guī)模數(shù)據(jù)。

　　And both are enforcing licensing rules: for instance, that a stream of tweets can be analysedbut not republished.

　　兩家也都在實施許可規(guī)則：比如，一系列的推文可以用來分析，但是不能再次將其發(fā)布。

　　Gnip, based in Boulder, Colorado, is more of a wholesale distributor. It charges $33,000 amonth for a feed of half of all tweets.

　　位于科羅拉多的博爾德的Gnip公司更大程度上是一個批發(fā)型分銷商。

　　Customers can also subscribe to feeds of tweets containing web links or certain keywords.

　　訂閱全部推文的一半的消息源，要收3.3萬美元每月。

　　Buyers are mostly social-media monitoring companies, which analyse the data for a fee.

　　客戶也可以訂閱包含網(wǎng)址或某些特定關(guān)鍵字的消息源。

　　Sysomos, a Canadian firm, for example, allows firms to track in real time what people thinkabout certain products.

　　買方通常是社交媒體監(jiān)控企業(yè)，它們靠分析數(shù)據(jù)收費賺錢。例如，Sysomos是一個加拿大公司，它可以讓企業(yè)實時追蹤人們對于特定產(chǎn)品的感受。

　　DataSift serves both big corporations and individuals.

　　DataSift的服務(wù)對象包括大企業(yè)和個人。

　　Customers can define sophisticated filters, for instance to find all tweets by men who areinterested in a new product and live in London.

　　客戶可以定義復(fù)雜的過濾條件，例如找到對一個新產(chǎn)品有興趣，且在倫敦生活的男性所發(fā)的所有推文。

　　Charges for DataSift depend on the filter s complexity and the amount of data delivered.

　　DataSift的費用取決于過濾條件的復(fù)雜程度和交付的數(shù)據(jù)流量。

　　The streams from Gnip and DataSift can be combined with data from more specialised firmsthat try to extract meaning from social-media data.

　　Gnip和DataSift的數(shù)據(jù)流可以與更專業(yè)的企業(yè)的數(shù)據(jù)結(jié)合，那些更專業(yè)的企業(yè)試圖從社交媒體數(shù)據(jù)中提取出意義。

　　Lexalytics, for instance, analyses the sentiment of messages and posts.

　　比如，Lexalytics分析消息和帖子的情緒。

　　Klout measures the influence of social-media users .

　　Klout則測量社交媒體用戶的影響力。

　　Having a marketplace such as DataSift has already encouraged other social-media servicesto open their data vaults, says Nick Halstead, the founder of DataSift.

　　DataSift的創(chuàng)始人尼克?霍爾斯特德說，有了像DataSift那樣的數(shù)據(jù)交易市場，使得更多的社交媒體服務(wù)商開啟其數(shù)據(jù)的倉庫。

　　Financial firms have become interested in feeding such data into the algorithms they use tomake investment decisions, says Chris Moody, Gnip s president.

　　Gnip的總裁克里斯?穆迪說，金融企業(yè)對此也有興趣：將這樣的數(shù)據(jù)放到他們的算法里，來做出投資決策。

　　And corporations are increasingly keen on combining social-media data with customerinformation.

　　企業(yè)越來越熱衷于將社交媒體數(shù)據(jù)和客戶信息相結(jié)合。

　　Yet growth in this market could be held backby privacy concerns.

　　然而，對于隱私的擔(dān)憂可能會阻礙這個市場的發(fā)展。

　　Most people think that tweets are only up to 140 characters long.

　　多數(shù)人認(rèn)為一條推文最多只有140個字符那么長。

　　But those who sip from Twitter s fire hose can get much more information, including asender s location, the biography on his profile page and how many people have subscribedto his messages .

　　但是那些從推特的數(shù)據(jù)洪流中 細(xì)細(xì)品味 的人，可以從中得到更多的信息，包括發(fā)送者的所在位置，從個人介紹頁面得到的檔案信息，以及有多少人訂閱了他的信息。

　　Most of this information is freely available on Twitter s website.

　　多數(shù)這樣的信息都是可以從推特的網(wǎng)站上免費獲取的。

　　But if users realise how their data are used, they may clam up.

　　但是，一旦用戶意識到他們的數(shù)據(jù)是怎樣被使用的，他們可能就會緘口不言了。